Abstract
In this paper we present a method for fully automatic left atrium segmentation from 3D cardiac magnetic resonance datasets. We propose a machine learning approach using decision forests that requires very few assumptions on the segmentation problem. First, we extract the blood pool using a simple thresholding technique. Then, we learn to separate the left atrium from other structures in the image by using context-rich features applied on images enhanced with a multi-scale vesselness filter and transformed to measure distance to blood pool surface. We present our results on the STACOM LA Segmentation Challenge 2013 validation datasets.
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Margeta, J., McLeod, K., Criminisi, A., Ayache, N. (2014). Decision Forests for Segmentation of the Left Atrium from 3D MRI. In: Camara, O., Mansi, T., Pop, M., Rhode, K., Sermesant, M., Young, A. (eds) Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges. STACOM 2013. Lecture Notes in Computer Science, vol 8330. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54268-8_6
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DOI: https://doi.org/10.1007/978-3-642-54268-8_6
Publisher Name: Springer, Berlin, Heidelberg
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